Managing redundancy among application bundles

ABSTRACT

Disclosed aspects include managing an application bundle for processing a stream of tuples. A set of application bundle data related to both a set of compute nodes and the application bundle is monitored by a first compute node. A determination is made that the application bundle is installed on fewer than a threshold number of compute nodes by the first compute node based on the set of application bundle data. The application bundle is retrieved by the first compute node in response to determining the application bundle is installed on fewer than a threshold number of compute nodes. The application bundle is installed on the first compute node by the first compute node in response to retrieving the application bundle.

STATEMENT REGARDING PRIOR DISCLOSURES BY THE INVENTOR OR A JOINTINVENTOR

The following disclosure(s) are submitted under 35 U.S.C. 102(b)(1)(A):An open invitation to beta-test IBM InfoSphere Streams, Oct. 20, 2014.

BACKGROUND

This disclosure relates generally to computer systems and, moreparticularly, relates to stream computing. Database systems aretypically configured to separate the process of storing data fromaccessing, manipulating, or using data stored in a database. Morespecifically, database systems use a model in which data is first storedand indexed in a memory before subsequent querying and analysis. Ingeneral, database systems may not be well suited for performingreal-time processing and analyzing streaming data. In particular,database systems may be unable to store, index, and analyze largeamounts of streaming data efficiently or in real time.

SUMMARY

Aspects of the disclosure relate to efficient application bundleprovisioning with respect to stream computing. Aspects may manage one ormore application bundles by a local host which may be distributed withrespect to an operational control host. Backup of an application bundlemay occur using a set of application bundle data and be based onsusceptibility/vulnerability of the application bundle.

Disclosed aspects include managing an application bundle for processinga stream of tuples. A set of application bundle data related to both aset of compute nodes and the application bundle is monitored by a firstcompute node. A determination is made that the application bundle isinstalled on fewer than a threshold number of compute nodes by the firstcompute node based on the set of application bundle data. Theapplication bundle is retrieved by the first compute node in response todetermining the application bundle is installed on fewer than athreshold number of compute nodes. The application bundle is installedon the first compute node by the first compute node in response toretrieving the application bundle.

The above summary is not intended to describe each illustratedembodiment or every implementation of the present disclosure.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The drawings included in the present application are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 illustrates a computing infrastructure configured to execute astream computing application according to various embodiments.

FIG. 2 illustrates a more detailed view of a compute node of FIG. 1according to various embodiments.

FIG. 3 illustrates a more detailed view of the management system of FIG.1 according to various embodiments.

FIG. 4 illustrates a more detailed view of the compiler system of FIG. 1according to various embodiments.

FIG. 5 illustrates an operator graph for a stream computing applicationaccording to various embodiments.

FIG. 6 shows an example system for using application bundle managementwith respect to executing a stream computing application according toembodiments.

FIG. 7 is a flowchart illustrating a method for processing a stream oftuples using an application bundle according to embodiments.

FIG. 8 is a flowchart illustrating a method for managing a set ofapplication bundles for processing a stream of tuples according toembodiments.

FIG. 9 is a flowchart illustrating a method for managing an applicationbundle for processing a stream of tuples according to embodiments.

FIG. 10 is a flowchart illustrating a method for managing an applicationbundle for processing a stream of tuples according to embodiments.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

DETAILED DESCRIPTION

Aspects of the disclosure relate to efficient application bundleprovisioning with respect to stream computing. Aspects may manage one ormore application bundles by a local host which may be distributed withrespect to an operational control host.

Provisioning/installation of the one or more application bundles mayoccur using a “pull” methodology by the local host. Preloading (e.g.,loading an application bundle on a compute node before the applicationbundle is needed) can occur in advance of receiving the stream of tuplesfor expected processing. Staleness latency of an application bundle mayoccur using a methodology which keeps the application bundle on acompute node longer than the application bundle is needed at the time.Backup of an application bundle may occur using a set of applicationbundle data and be based on susceptibility/vulnerability of theapplication bundle.

Stream-based computing and stream-based database computing are emergingas a developing technology for database systems. Products are availablewhich allow users to create applications that process and querystreaming data before it reaches a database file. With this emergingtechnology, users can specify processing logic to apply to inbound datarecords while they are “in flight,” with the results available in a veryshort amount of time, often in fractions of a second. Constructing anapplication using this type of processing has opened up a newprogramming paradigm that will allow for development of a broad varietyof innovative applications, systems, and processes, as well as presentnew challenges for application programmers and database developers.

In a stream computing application, stream operators are connected to oneanother such that data flows from one stream operator to the next (e.g.,over a TCP/IP socket). When a stream operator receives data, it mayperform operations, such as analysis logic, which may change the tupleby adding or subtracting attributes, or updating the values of existingattributes within the tuple. When the analysis logic is complete, a newtuple is then sent to the next stream operator. Scalability is achievedby distributing an application across nodes by creating executables(i.e., processing elements), as well as replicating processing elementson multiple nodes and load balancing among them. Stream operators in astream computing application can be fused together to form a processingelement that is executable. Doing so allows processing elements to sharea common process space, resulting in much faster communication betweenstream operators than is available using inter-process communicationtechniques (e.g., using a TCP/IP socket). Further, processing elementscan be inserted or removed dynamically from an operator graphrepresenting the flow of data through the stream computing application.A particular stream operator may not reside within the same operatingsystem process as other stream operators. In addition, stream operatorsin the same operator graph may be hosted on different nodes, e.g., ondifferent compute nodes or on different cores of a compute node.

Data flows from one stream operator to another in the form of a “tuple.”A tuple is a sequence of one or more attributes associated with anentity. Attributes may be any of a variety of different types, e.g.,integer, float, Boolean, string, etc. The attributes may be ordered. Inaddition to attributes associated with an entity, a tuple may includemetadata, i.e., data about the tuple. A tuple may be extended by addingone or more additional attributes or metadata to it. As used herein,“stream” or “data stream” refers to a sequence of tuples. Generally, astream may be considered a pseudo-infinite sequence of tuples.

Tuples are received and output by stream operators and processingelements. An input tuple corresponding with a particular entity that isreceived by a stream operator or processing element, however, isgenerally not considered to be the same tuple that is output by thestream operator or processing element, even if the output tuplecorresponds with the same entity or data as the input tuple. An outputtuple need not be changed in some way from the input tuple.

Nonetheless, an output tuple may be changed in some way by a streamoperator or processing element. An attribute or metadata may be added,deleted, or modified. For example, a tuple will often have two or moreattributes. A stream operator or processing element may receive thetuple having multiple attributes and output a tuple corresponding withthe input tuple. The stream operator or processing element may onlychange one of the attributes so that all of the attributes of the outputtuple except one are the same as the attributes of the input tuple.

Generally, a particular tuple output by a stream operator or processingelement may not be considered to be the same tuple as a correspondinginput tuple even if the input tuple is not changed by the processingelement. However, to simplify the present description and the claims, anoutput tuple that has the same data attributes or is associated with thesame entity as a corresponding input tuple will be referred to herein asthe same tuple unless the context or an express statement indicatesotherwise.

Stream computing applications handle massive volumes of data that needto be processed efficiently and in real time. For example, a streamcomputing application may continuously ingest and analyze hundreds ofthousands of messages per second and up to petabytes of data per day.Accordingly, each stream operator in a stream computing application maybe required to process a received tuple within fractions of a second.Unless the stream operators are located in the same processing element,it is necessary to use an inter-process communication path each time atuple is sent from one stream operator to another. Inter-processcommunication paths can be a critical resource in a stream computingapplication. According to various embodiments, the available bandwidthon one or more inter-process communication paths may be conserved.Efficient use of inter-process communication bandwidth can speed upprocessing.

A streams processing job has a directed graph of processing elementsthat send data tuples between the processing elements. The processingelement operates on the incoming tuples, and produces output tuples. Aprocessing element has an independent processing unit and runs on ahost. The streams platform can be made up of a collection of hosts thatare eligible for processing elements to be placed upon. When a job issubmitted to the streams run-time, the platform scheduler processes theplacement constraints on the processing elements, and then determines(the best) one of these candidates host for (all) the processingelements in that job, and schedules them for execution on the decidedhost.

In order for the processing element to be executed on the targeted host,the executable code image for the job (application bundle) may be madeavailable on that host. Other than job submission, application bundlemanagement may also be performed when a processing element gets moved toa new host. Typical reasons for why processing elements get moved to anew host are for load-balancing purposes, or for failover scenarios whena host goes down. If the new host does not have the application bundleavailable, then it can be provisioned there. Application bundles can berelatively large, so there is a measurable cost of moving applicationbundles between hosts and for storing them on a host.

Options for making application bundles available on relevant host(s)include downloading the application bundle as part of a processingelement start-up request (e.g., startPE) and storing the applicationbundle (into a non-shared or shared file system). In the firstmethodology (downloading as part of a start-up “push”), there is only asingle source point for downloads, the central control point can onlyexhibit limited control over the application bundle management on eachhost, the central control point may desire to control when to uninstallthe application bundle as well, and it may be cumbersome to implementmore advanced management operations (e.g., staleness detection,efficient redundancy). In the second methodology (storing into a filesystem), in certain systems the hosts may have to mount/manage withrespect to the file system. Aspects of the disclosure may provideperformance or efficiency benefits when managing one or more applicationbundles (e.g., speed, flexibility, responsiveness, resource usage).

FIG. 1 illustrates one exemplary computing infrastructure 100 that maybe configured to execute a stream computing application, according tosome embodiments. The computing infrastructure 100 includes a managementsystem 105 and two or more compute nodes 110A-110D—i.e., hosts—which arecommunicatively coupled to each other using one or more communicationsnetworks 120. The communications network 120 may include one or moreservers, networks, or databases, and may use a particular communicationprotocol to transfer data between the compute nodes 110A-110D. Acompiler system 102 may be communicatively coupled with the managementsystem 105 and the compute nodes 110 either directly or via thecommunications network 120.

The communications network 120 may include a variety of types ofphysical communication channels or “links.” The links may be wired,wireless, optical, or any other suitable media. In addition, thecommunications network 120 may include a variety of network hardware andsoftware for performing routing, switching, and other functions, such asrouters, switches, or bridges. The communications network 120 may bededicated for use by a stream computing application or shared with otherapplications and users. The communications network 120 may be any size.For example, the communications network 120 may include a single localarea network or a wide area network spanning a large geographical area,such as the Internet. The links may provide different levels ofbandwidth or capacity to transfer data at a particular rate. Thebandwidth that a particular link provides may vary depending on avariety of factors, including the type of communication media andwhether particular network hardware or software is functioning correctlyor at full capacity. In addition, the bandwidth that a particular linkprovides to a stream computing application may vary if the link isshared with other applications and users. The available bandwidth mayvary depending on the load placed on the link by the other applicationsand users. The bandwidth that a particular link provides may also varydepending on a temporal factor, such as time of day, day of week, day ofmonth, or season.

FIG. 2 is a more detailed view of a compute node 110, which may be thesame as one of the compute nodes 110A-110D of FIG. 1, according tovarious embodiments. The compute node 110 may include, withoutlimitation, one or more processors (CPUs) 205, a network interface 215,an interconnect 220, a memory 225, and a storage 230. The compute node110 may also include an I/O device interface 210 used to connect I/Odevices 212, e.g., keyboard, display, and mouse devices, to the computenode 110.

Each CPU 205 retrieves and executes programming instructions stored inthe memory 225 or storage 230. Similarly, the CPU 205 stores andretrieves application data residing in the memory 225. The interconnect220 is used to transmit programming instructions and application databetween each CPU 205, I/O device interface 210, storage 230, networkinterface 215, and memory 225. The interconnect 220 may be one or morebusses. The CPUs 205 may be a single CPU, multiple CPUs, or a single CPUhaving multiple processing cores in various embodiments. In oneembodiment, a processor 205 may be a digital signal processor (DSP). Oneor more processing elements 235 (described below) may be stored in thememory 225. A processing element 235 may include one or more streamoperators 240 (described below). In one embodiment, a processing element235 is assigned to be executed by only one CPU 205, although in otherembodiments the stream operators 240 of a processing element 235 mayinclude one or more threads that are executed on two or more CPUs 205.The memory 225 is generally included to be representative of a randomaccess memory, e.g., Static Random Access Memory (SRAM), Dynamic RandomAccess Memory (DRAM), or Flash. The storage 230 is generally included tobe representative of a non-volatile memory, such as a hard disk drive,solid state device (SSD), or removable memory cards, optical storage,flash memory devices, network attached storage (NAS), or connections tostorage area network (SAN) devices, or other devices that may storenon-volatile data. The network interface 215 is configured to transmitdata via the communications network 120.

A stream computing application may include one or more stream operators240 that may be compiled into a “processing element” container 235. Thememory 225 may include two or more processing elements 235, eachprocessing element having one or more stream operators 240. Each streamoperator 240 may include a portion of code that processes tuples flowinginto a processing element and outputs tuples to other stream operators240 in the same processing element, in other processing elements, or inboth the same and other processing elements in a stream computingapplication. Processing elements 235 may pass tuples to other processingelements that are on the same compute node 110 or on other compute nodesthat are accessible via communications network 120. For example, aprocessing element 235 on compute node 110A may output tuples to aprocessing element 235 on compute node 110B.

The storage 230 may include a buffer 260. Although shown as being instorage, the buffer 260 may be located in the memory 225 of the computenode 110 or in a combination of both memories. Moreover, storage 230 mayinclude storage space that is external to the compute node 110, such asin a cloud.

The compute node 110 may include one or more operating systems 262. Anoperating system 262 may be stored partially in memory 225 and partiallyin storage 230. Alternatively, an operating system may be storedentirely in memory 225 or entirely in storage 230. The operating systemprovides an interface between various hardware resources, including theCPU 205, and processing elements and other components of the streamcomputing application. In addition, an operating system provides commonservices for application programs, such as providing a time function.

FIG. 3 is a more detailed view of the management system 105 of FIG. 1according to some embodiments. The management system 105 may include,without limitation, one or more processors (CPUs) 305, a networkinterface 315, an interconnect 320, a memory 325, and a storage 330. Themanagement system 105 may also include an I/O device interface 310connecting I/O devices 312, e.g., keyboard, display, and mouse devices,to the management system 105.

Each CPU 305 retrieves and executes programming instructions stored inthe memory 325 or storage 330. Similarly, each CPU 305 stores andretrieves application data residing in the memory 325 or storage 330.The interconnect 320 is used to move data, such as programminginstructions and application data, between the CPU 305, I/O deviceinterface 310, storage unit 330, network interface 315, and memory 325.The interconnect 320 may be one or more busses. The CPUs 305 may be asingle CPU, multiple CPUs, or a single CPU having multiple processingcores in various embodiments. In one embodiment, a processor 305 may bea DSP. Memory 325 is generally included to be representative of a randomaccess memory, e.g., SRAM, DRAM, or Flash. The storage 330 is generallyincluded to be representative of a non-volatile memory, such as a harddisk drive, solid state device (SSD), removable memory cards, opticalstorage, Flash memory devices, network attached storage (NAS),connections to storage area-network (SAN) devices, or the cloud. Thenetwork interface 315 is configured to transmit data via thecommunications network 120.

The memory 325 may store a stream manager 134. Additionally, the storage330 may store an operator graph 335. The operator graph 335 may definehow tuples are routed to processing elements 235 (FIG. 2) forprocessing.

The management system 105 may include one or more operating systems 332.An operating system 332 may be stored partially in memory 325 andpartially in storage 330. Alternatively, an operating system may bestored entirely in memory 325 or entirely in storage 330. The operatingsystem provides an interface between various hardware resources,including the CPU 305, and processing elements and other components ofthe stream computing application. In addition, an operating systemprovides common services for application programs, such as providing atime function.

FIG. 4 is a more detailed view of the compiler system 102 of FIG. 1according to some embodiments. The compiler system 102 may include,without limitation, one or more processors (CPUs) 405, a networkinterface 415, an interconnect 420, a memory 425, and storage 430. Thecompiler system 102 may also include an I/O device interface 410connecting I/O devices 412, e.g., keyboard, display, and mouse devices,to the compiler system 102.

Each CPU 405 retrieves and executes programming instructions stored inthe memory 425 or storage 430. Similarly, each CPU 405 stores andretrieves application data residing in the memory 425 or storage 430.The interconnect 420 is used to move data, such as programminginstructions and application data, between the CPU 405, I/O deviceinterface 410, storage unit 430, network interface 415, and memory 425.The interconnect 420 may be one or more busses. The CPUs 405 may be asingle CPU, multiple CPUs, or a single CPU having multiple processingcores in various embodiments. In one embodiment, a processor 405 may bea DSP. Memory 425 is generally included to be representative of a randomaccess memory, e.g., SRAM, DRAM, or Flash. The storage 430 is generallyincluded to be representative of a non-volatile memory, such as a harddisk drive, solid state device (SSD), removable memory cards, opticalstorage, flash memory devices, network attached storage (NAS),connections to storage area-network (SAN) devices, or to the cloud. Thenetwork interface 415 is configured to transmit data via thecommunications network 120.

The compiler system 102 may include one or more operating systems 432.An operating system 432 may be stored partially in memory 425 andpartially in storage 430. Alternatively, an operating system may bestored entirely in memory 425 or entirely in storage 430. The operatingsystem provides an interface between various hardware resources,including the CPU 405, and processing elements and other components ofthe stream computing application. In addition, an operating systemprovides common services for application programs, such as providing atime function.

The memory 425 may store a compiler 136. The compiler 136 compilesmodules, which include source code or statements, into the object code,which includes machine instructions that execute on a processor. In oneembodiment, the compiler 136 may translate the modules into anintermediate form before translating the intermediate form into objectcode. The compiler 136 may output a set of deployable artifacts that mayinclude a set of processing elements and an application descriptionlanguage file (ADL file), which is a configuration file that describesthe stream computing application. In some embodiments, the compiler 136may be a just-in-time compiler that executes as part of an interpreter.In other embodiments, the compiler 136 may be an optimizing compiler. Invarious embodiments, the compiler 136 may perform peepholeoptimizations, local optimizations, loop optimizations, inter-proceduralor whole-program optimizations, machine code optimizations, or any otheroptimizations that reduce the amount of time required to execute theobject code, to reduce the amount of memory required to execute theobject code, or both. The output of the compiler 136 may be representedby an operator graph, e.g., the operator graph 335.

The compiler 136 may also provide the application administrator with theability to optimize performance through profile-driven fusionoptimization. Fusing operators may improve performance by reducing thenumber of calls to a transport. While fusing stream operators mayprovide faster communication between operators than is available usinginter-process communication techniques, any decision to fuse operatorsrequires balancing the benefits of distributing processing acrossmultiple compute nodes with the benefit of faster inter-operatorcommunications. The compiler 136 may automate the fusion process todetermine how to best fuse the operators to be hosted by one or moreprocessing elements, while respecting user-specified constraints. Thismay be a two-step process, including compiling the application in aprofiling mode and running the application, then re-compiling and usingthe optimizer during this subsequent compilation. The end result may,however, be a compiler-supplied deployable application with an optimizedapplication configuration.

FIG. 5 illustrates an exemplary operator graph 500 for a streamcomputing application beginning from one or more sources 135 through toone or more sinks 504, 506, according to some embodiments. This flowfrom source to sink may also be generally referred to herein as anexecution path. In addition, a flow from one processing element toanother may be referred to as an execution path in various contexts.Although FIG. 5 is abstracted to show connected processing elementsPE1-PE10, the operator graph 500 may include data flows between streamoperators 240 (FIG. 2) within the same or different processing elements.Typically, processing elements, such as processing element 235 (FIG. 2),receive tuples from the stream as well as output tuples into the stream(except for a sink—where the stream terminates, or a source—where thestream begins). While the operator graph 500 includes a relatively smallnumber of components, an operator graph may be much more complex and mayinclude many individual operator graphs that may be statically ordynamically linked together.

The example operator graph shown in FIG. 5 includes ten processingelements (labeled as PE1-PE10) running on the compute nodes 110A-110D. Aprocessing element may include one or more stream operators fusedtogether to form an independently running process with its own processID (PID) and memory space. In cases where two (or more) processingelements are running independently, inter-process communication mayoccur using a “transport,” e.g., a network socket, a TCP/IP socket, orshared memory. Inter-process communication paths used for inter-processcommunications can be a critical resource in a stream computingapplication. However, when stream operators are fused together, thefused stream operators can use more rapid communication techniques forpassing tuples among stream operators in each processing element.

The operator graph 500 begins at a source 135 and ends at a sink 504,506. Compute node 110A includes the processing elements PE1, PE2, andPE3. Source 135 flows into the processing element PE1, which in turnoutputs tuples that are received by PE2 and PE3. For example, PE1 maysplit data attributes received in a tuple and pass some data attributesin a new tuple to PE2, while passing other data attributes in anothernew tuple to PE3. As a second example, PE1 may pass some received tuplesto PE2 while passing other tuples to PE3. Tuples that flow to PE2 areprocessed by the stream operators contained in PE2, and the resultingtuples are then output to PE4 on compute node 110B. Likewise, the tuplesoutput by PE4 flow to operator sink PE6 504. Similarly, tuples flowingfrom PE3 to PE5 also reach the operators in sink PE6 504. Thus, inaddition to being a sink for this example operator graph, PE6 could beconfigured to perform a join operation, combining tuples received fromPE4 and PE5. This example operator graph also shows tuples flowing fromPE3 to PE7 on compute node 110C, which itself shows tuples flowing toPE8 and looping back to PE7. Tuples output from PE8 flow to PE9 oncompute node 110D, which in turn outputs tuples to be processed byoperators in a sink processing element, for example PE10 506.

Processing elements 235 (FIG. 2) may be configured to receive or outputtuples in various formats, e.g., the processing elements or streamoperators could exchange data marked up as XML documents. Furthermore,each stream operator 240 within a processing element 235 may beconfigured to carry out any form of data processing functions onreceived tuples, including, for example, writing to database tables orperforming other database operations such as data joins, splits, reads,etc., as well as performing other data analytic functions or operations.

The stream manager 134 of FIG. 1 may be configured to monitor a streamcomputing application running on compute nodes, e.g., compute nodes110A-110D, as well as to change the deployment of an operator graph,e.g., operator graph 132. The stream manager 134 may move processingelements from one compute node 110 to another, for example, to managethe processing loads of the compute nodes 110A-110D in the computinginfrastructure 100. Further, stream manager 134 may control the streamcomputing application by inserting, removing, fusing, un-fusing, orotherwise modifying the processing elements and stream operators (orwhat tuples flow to the processing elements) running on the computenodes 110A-110D.

Because a processing element may be a collection of fused streamoperators, it is equally correct to describe the operator graph as oneor more execution paths between specific stream operators, which mayinclude execution paths to different stream operators within the sameprocessing element. FIG. 5 illustrates execution paths betweenprocessing elements for the sake of clarity.

FIG. 6 shows an example system 600 for using application bundlemanagement with respect to executing a stream computing applicationaccording to embodiments. The example system 600 may illustrate a filesystem. The example system 600 has a Host A 610 and a Host B 620(“hosts”). Aspects of the hosts 610, 620 may have similar or the samecomponents with like functionality. The hosts 610, 620 may haveprocessors 618, 628 and memory 619, 629. In embodiments, the hosts arephysically separate compute nodes. In certain embodiments, virtualmachines may be utilized. Lines/arrows may be depicted to show certaincommunications of the example system 600; however, other communicationsbetween/among components are contemplated.

In response to receiving a job (e.g., a job with an associatedapplication bundle), a scheduler 605 (i.e., stream manager) can identifyfor allocation and allocate processing elements to hosts 610, 620. Thescheduler 605 may monitor a stream application running on the hosts 610,620. As such, the scheduler 605 can change the deployment of theoperator graph. The scheduler 605 may, for example, move processingelements from one host to another to manage the processing loads of thehosts 610, 620 in the system 600. The scheduler 605 may communicate withcomputer processing element controllers 612, 622. For example, thescheduler 605 may send determine/select placement constraints onprocessing elements for the job, determine a candidate host for theprocessing elements for the job (e.g., Host B 620), and schedule theprocessing elements for the job for execution on the candidate host.

Computer processing element controllers 612, 622 can manage a life-cycleof one or more computer processing elements. A portion of the life-cycleincludes getting the one or more processing elements started.Accordingly, the one or more computer processing elements can correspondwith an executable bundle, and the executable bundle may be madeavailable on the host (e.g., via a request to an application bundlecontroller to load/install the bundle). In response to the processingelement controllers 612, 622 starting the one or more computerprocessing elements, the one or more computer processing elements areactive for processing of tuples that flow through. so there is really noassignments of tasks to the PEs from the PEC.

Application bundle controllers 621, 611 manage a set of applicationbundles. For instance, the set of application bundles may be included ina file system which has individual hosts performing mounting/management(e.g., by the application bundle controllers 621, 611). In order for aparticular processing element to be executed on a particular host, anapplication bundle (e.g., executable code image for the job) may beavailable/installed with respect to the particular host. Applicationbundle controllers 621, 611 may be involved in provisioning theparticular application bundle (e.g., communicate with each other). Forexample, the particular application may be desired when a job issubmitted. In embodiments, processing elements may be moved from onehost to another (e.g., Host B 620 to Host A 610) for reasons such asload-balancing or failover circumstances. As such, the new host (e.g.,Host A 610) may get/retrieve/pull the application bundle. Movingapplication bundles (which can include file sizes such 0.5 gigabytes)can have a measurable cost (a variety of sizes may have a measurablecost and can be covered by embodiments). Aspects of an applicationbundle controller 621/611 may have performance or efficiency benefits byoperating local to a host 620/610. Local information may be leveraged(e.g., databases on the local host having worthwhile information may beutilized). Management may occur proactively (e.g., instead of in areactive manner).

In response to receiving a “get application bundle request” (e.g., fromcomputer processing element controller 622), the application bundlecontroller 621 can query/check an application bundle directory 607(e.g., which can be global to all hosts within the example system 600)to ascertain where the current locations are for the application bundle(e.g., a set of candidate application bundle sources). For example,application bundle directory 607 may indicate that both Host A 610 andHost C (not shown) have a first application bundle and may indicate thatHost B 620 has a second application bundle (e.g., lists which hostscurrently have which application bundles installed on it). Using the setof candidate application bundle sources, the application bundlecontroller 621 may determine/select a specific source with which toinitiate retrieval/download/installation (e.g., provision management).The determination/selection can be based on a variety of factors such ashost proximity (e.g., network-wise nearness) with expected transferrates above a threshold, round-robin distribution for spreading, randomselection, etc.

An application bundle monitor 625 thread can (periodically) makeadjustments as to which application bundles that are not being utilizedat a temporal point (e.g., not required currently) by a runningprocessing element may get installed/un-installed on Host B 620 (e.g.,the host it resides on and is monitoring). In embodiments, oneapplication bundle monitor thread runs on each host and makesdecisions/determinations on its own set of policies (e.g., policychoices are made separately for each host). For example, the applicationbundle monitor 625 determines policies 627 for Host B 620. Similarly,the application bundle monitor 625 can monitor/determine/impact/selectresource usage 626 (e.g., file space=80%: CPU=24%) using, for example,threshold values. Also, the application bundle monitor 625 canobserve/track relationships (e.g., status, configuration) of processingelements and application bundles using a processing element table 624(e.g., a first processing element using a specific application bundle, asecond processing element using the specific application bundle). Theprocessing element table 624 may be local to each host and can listwhich processing elements are currently running on its host along withwhich application bundle it references.

A job table 609 can record for observation/tracking (e.g., status,configuration) of application bundles and hosts (e.g., a firstapplication bundle on Host A 610 and Host B 620: a second applicationbundle on Host A 610 and Host C (not shown)). Candidates to hostprocessing elements for a particular job may be identified/listed in thejob table 609 (e.g., which can be global to all hosts within the examplesystem 600 and may be indexed by application bundle). Application bundlerepositories 613, 623 can store one or more application bundles (and betracked/monitored/observed in ways described herein). Otherconfigurations and arrangements are contemplated. Consider the followingillustrative operation flow when managing application bundle(s) usingthe example system 600.

To begin, a submission may be received. The submission may include ajob, and be related to a set of processing elements and associated withan application bundle. Using the scheduler 605, a runtime determinationis made for assignment of the set of processing elements to a set ofhosts (e.g., Host A 610, Host B 620). The scheduler 605 can thentransmit/send a request (e.g., to computer processing element controller622) having a (remote) execute start processing element command for aparticular processing element on a particular host (e.g., PE1 on Host B620).

Processing element start-up (e.g., by the computer processing elementcontroller 622) may work with a local application bundle controller(e.g., application bundle controller 621) to determine if theapplication bundle is installed on the local host (e.g., Host B 620). Ifit is determined that the application bundle is not installed on thelocal host, then the local application bundle controller (e.g., 621)queries the application bundle directory (e.g., 607) for candidatehosts. A particular candidate host (e.g., 610) may be identified. Thelocal application bundle controller (e.g., 621) can then pull theapplication bundle from the particular candidate host (e.g., 610). Atleast one of communicating, pulling, downloading, or installing mayoccur utilizing the application bundle controllers 611, 621.

In response to the local application bundle controller (e.g., 621)pulling the application bundle, processing elementstart-up/initiation/execution may be continued. Application bundlecontrollers (e.g., 611, 621) can manage operations (e.g., preloading,staleness latency, backup) on individual respective hosts whenprocessing the job. Accordingly, aspects of example system 600 mayprovide performance or efficiency benefits (e.g., speed, flexibility,responsiveness, resource usage) when managing a code load (e.g., movingexecutable code between hosts) using a middle/local layer of applicationprogram management.

FIG. 7 is a flowchart illustrating a method 700 for processing a streamof tuples using an application bundle according to embodiments. Aspectscan manage application bundle(s) (in a file system) by a local hostwhich is distributed/separate from a central control host or a remotehost that execution is intended for with respect to the applicationbundle(s). Provisioning/installation of application bundle(s) may occurusing a “pull” methodology. Method 700 may begin at block 701.

At block 710, a stream of tuples is received. The stream of tuples is tobe processed by a plurality of processing elements operating on a set ofcompute nodes. The stream of tuples may be received consistent with thedescription herein including FIGS. 1-6. Current/future processing by theplurality of processing elements may be performed consistent with thedescription herein including FIGS. 1-6. The set of compute nodes mayinclude a shared pool of configurable computing resources. For example,the set of compute nodes can be a public cloud environment, a privatecloud environment, or a hybrid cloud environment. In certainembodiments, each of the set of compute nodes are physically separatefrom one another.

At block 720, a determination is made to retrieve the application bundlefrom a second compute node. The determination is made by a first computenode based on a set of application bundle data. The set of applicationbundle data can include which nodes/hosts (currently) have whichapplication bundle(s). For instance, a multi-dimensional array mayinclude a first column listing entries for application bundles (e.g.,AB1, AB2, AB3) and a second column listing entries for compute nodes(e.g., HostA+HostC, HostB, HostA+HostB+HostC). Entries in the same rowof the multi-dimensional array can correlate application bundles withcompute nodes (e.g., AB1 is on Host A and Host C, AB2 is on Host B, AB3is on each of the three example hosts). Other datastructures/orientations are considered, the multi-dimensional array ispresented as illustrative.

In embodiments, the set of application bundle data may be storedseparate from both the first compute node and the second compute node(see block 731). The set of application bundle data may be globallyavailable to (all) nodes/hosts. In various example embodiments, the setof application bundle data may be available via a pull model, afetch/retrive model, a publish/subscribe model, or a push model. Assuch, an application bundle controller may have a connection with anapplication bundle directory for retrieval of candidate locations of aspecific application bundle. In embodiments, the determination caninclude an analyzing operation or an identification operation.

In embodiments, the set of application bundle data is analyzed at block721. For instance, analyzing can include examining (e.g., performing aninspection of host/bundle-data), evaluating (e.g., generating anappraisal of host/bundle-data), resolving (e.g., ascertaining anobservation/conclusion/answer with respect to host/bundle-data), parsing(e.g., deciphering a construct for the host/bundle-data), querying(e.g., asking a question regarding host/bundle-data), or categorizing(e.g., organizing by a feature of the host/bundle-data). Data analysismay include a process of inspecting, cleaning, transforming, or modelingdata to discover useful information, suggest conclusions, or supportdecisions. Data analysis can extract information/patterns from a dataset and transform/translate it into an understandable structure forfurther use. A criterion can be identified at block 722. The criterionmay be identified in response to and based on the analysis at block 721.The criterion can indicate to retrieve the application bundle from thesecond compute node. The criterion may be related to a variety offactors discussed herein. For instance, the criterion may correlate withan advantageous/favorable source (e.g., provides overall performance orefficiency benefits for the system by sourcing from a more favorablesource rather than a less favorable source).

In certain embodiments, the set of application bundle data is examinedfor the application bundle at block 723. For example, an inspection ofthe set of application bundle data occurs to find a listing of nodeshaving the application bundle. It may be detected at block 724 that thesecond compute node has the application bundle. For example, theapplication bundle data can indicate possession/holding/keeping/runningof the application bundle by one or more compute nodes including thesecond compute node. Using (e.g., based on by analyzing) at least onesource criterion, it can be determined at block 725 to retrieve theapplication bundle from the second compute node.

In embodiments, determining to retrieve the application bundle from thesecond compute node can include using at least one source criterion(e.g., basis, benchmark, factor, measure, point of comparison, rule forsourcing). The at least one source criterion can include at least one ofa physical proximity criterion, an expected resource burden criterion, autilization frequency criterion, or a random selection criterion. Atblock 726, a physical proximity criterion of the second compute nodewith respect to the first compute node may be used. For example,nearness may be measured with respect to geographic location which mayindicate positive impacts on performance/efficiency bandwidth or speedwhen compared with another node. At block 727, an expected resourceburden criterion when downloading the application bundle to the firstcompute node from the second compute node can be used. For instance,processing or memory usage may be different depending on which computenodes are used for reasons such as compatibility or activity. At block728, a utilization frequency criterion of the second compute node may beused. For example, a count of how often the second compute node is usedfor processing may be recorded so that resources/assets may bedistributed without causing an undue burden on nodes which perform theprocessing or so as to assist those nodes perform the processing. Atblock 729, a random selection criterion of the second compute node canbe used. For instance, randomly choosing a source node can efficientlydistribute burdens or have security benefits. In certain embodiments, around-robin methodology or a combination of the various criteria may beused.

In embodiments, the application bundle is retrieved at block 735. Theapplication bundle may be retrieved by the first compute node (e.g.,pulled by the first compute node from the second compute node). Theapplication bundle may be retrieved in response to determining toretrieve the application bundle from the second compute node. Retrievingthe application bundle may include copying or moving the applicationbundle. Retrieving the application bundle can include at least one oftransmitting a request for the application bundle, obtaining theapplication bundle, or receiving the application bundle. In certainembodiments, retrieving can include selectively retrieving chosenelements of the application bundle to subsequently generate a likeversion of the application bundle by a piecemeal methodology.

At block 740, the application bundle is installed on the first computenode. The first compute node manages the installation. Using the firstcompute node to manage the installation can facilitate the pullmethodology. In embodiments, managing the installation can include, forexample, retrieval aspects (see block 735). Installing may includeinitiation/commencement of adeployment/placement/installation/allocation. Installing the applicationbundle may include creating a local copy/version of the applicationbundle on the first compute node. Installing the application bundle cangenerate a like version of the application bundle by a piecemealmethodology (e.g., fit together various components). Installation canhave a verification phase which verifies operability or completeness.Installation may include unwrapping a wrapped data packet (e.g., whichhas encryption technology for security/transmission purposes).

At block 750, the stream of tuples is processed. The stream of tuples isprocessed using the application bundle on the first compute node. Thestream of tuples may be processed consistent with the description hereinincluding FIGS. 1-6. The application bundle, having been pulled onto thefirst compute node, can process the stream of tuples without involvementfrom another compute node. Processing, using local management on thefirst compute node, of the stream of tuples may provide variousflexibilities for the set of compute nodes. Overall flow (e.g., dataflow) may be positively impacted by having the application bundle on thefirst compute node.

In embodiments, the set of application bundle data is updated to includethat the first compute node has the application bundle at block 760. Forexample, the first compute node can transmit a status notification forediting of the set of application bundle data. In embodiments, adetermination is made (by a third compute node based on the set ofapplication bundle data) to retrieve the application bundle from thefirst compute node at block 770. In response, the application bundle canbe retrieved by the third compute node. The application bundle may beinstalled on the third compute node (by the third compute node) at block780. As such, the first compute node can now be utilized just as thesecond compute node was previously. Therefore, pulling can permitparallelization of application bundle flow, download, or installation.

The methodology may provide an exponential-type benefit. To illustrate,a pull download can be performed in parallel between multiple source anddestination hosts. As hosts get the application bundle installed, theythen become available for a candidate source for a next applicationbundle download. So, a number of candidate sources for download requestsincreases. For example, if a job gets submitted to 8 hosts (A, B, C, D,E, F, G, H), an example of a possible bundle flow includes: 1) installedonto A; 2) B pulls from A; 3) C pulls from A and D pulls from B; 4) Epulls from A, F pulls from B, G pulls from C, and H pulls from D. Thisillustrative 4-phase flow to 8 hosts may provide performance orefficiency benefits with respect to, for example, an 8-operation flowusing a central control point to push the application bundle to each ofthe 8 hosts individually.

Method 700 concludes at block 799. Aspects of method 700 may provideperformance or efficiency benefits for processing a stream of tuplesusing an application bundle. For example, aspects of method 700 mayinclude positive impacts on resource usage using a pull operation withrespect to a particular application bundle in a file system. Altogether,performance or efficiency benefits when managing application bundle(s)may occur (e.g., speed, flexibility, responsiveness, resource usage).

FIG. 8 is a flowchart illustrating a method 800 for managing a set ofapplication bundles for processing a stream of tuples according toembodiments. Aspects can manage application bundle(s) (in a file system)by a local host which is distributed/separate from a central controlhost or a remote host that execution is intended for with respect to theapplication bundle(s). Preloading of application bundle(s) may occurusing a push or a pull methodology. Preloading can occurin-advance-of/separate-from receiving the stream of tuples. Aspects mayload/place an application bundle on a compute node before theapplication bundle is needed (for use). Preloading may be based onexpected processing of the stream of tuples. Method 800 may begin atblock 801.

At block 810, a first compute node monitors the set of applicationbundles which are installed on a second compute node. For example, thefirst compute node may directly communicate with the second compute nodeto perform the monitoring. Alternatively, the first compute node maycommunicate with an application bundle directory (e.g., set ofapplication bundle data) which is stored separate from the secondcompute node. In embodiments, monitoring the set of application bundlesmay include observing the set of application bundles at block 812. Also,monitoring the set of application bundles can include analyzing the setof application bundles at block 814. In certain embodiments, observingand analyzing can include aspects which receive an element and processthe element that was received.

For instance, observing (at block 812) can include querying (e.g.,asking a question), searching (e.g., exploring for a reason), obtaining(e.g., recording a collection), probing (e.g., checking a property),scanning (e.g., reviewing a sample), or tracking (e.g., following acharacteristic). To illustrate, observing may include locating a groupof specialized application bundles which can interpret theparticularized subset of information to be found in tuples expected tobe received for processing within a quickly-approaching temporal period.

For instance, analyzing (at block 814) can include extracting (e.g.,creating a derivation), examining (e.g., performing an inspection),dissecting (e.g., scrutinizing an attribute), parsing (e.g., decipheringa construct), comparing (e.g., relating an assessment), or classifying(e.g., assigning a designation). Data analysis may include a process ofinspecting, cleaning, transforming, or modeling data to discover usefulinformation, suggest conclusions, or support decisions. Data analysiscan extract information/patterns from a data set and transform/translateit into an understandable structure (e.g., a data report which can beprovided) for further use.

At block 830, the first compute node detects that the set of applicationbundles includes a candidate application bundle. Detecting the set ofapplication bundles includes the candidate application bundle caninclude sensing the set of application bundles includes the candidateapplication bundle. To illustrate, sensing can include identifying one(or more) candidate application bundle(s) of the group of specializedapplication bundles to interpret the particularized subset ofinformation to be found in tuples expected to be received for processingwithin the quickly-approaching temporal period (e.g., the next minute).In certain illustrative embodiments, detection can include anidentification of a measurable change/modification in, for example, anattribute related to the candidate application bundle (e.g., aquantitative representation of a ratio between a count of availablecandidate application bundles and a count of the set of applicationbundles).

At block 850, it is determined to install the candidate applicationbundle on the first compute node. Such determination is made by thefirst compute node based on a set of anticipation criteria. Thedetermination can include determining to utilize the first compute nodeto manage the installation by facilitating a pull methodology. Inembodiments, managing the installation can include, for example,retrieval aspects. In various embodiments, a push methodology (e.g.,from the second compute node via management external to the firstcompute node) may be determined to be utilized. Determining to installthe candidate application bundle may include determining a plan forinitiation/commencement of adeployment/placement/installation/allocation. The set of anticipationcriteria can have various factors, elements, or characteristics withrespect to certain foundations/grounds for installation of the candidateapplication bundle.

In embodiments, the determination can include analyzing the set ofanticipation criteria at block 852. Accordingly, the determination mayidentify a criterion at block 853. For instance, analyzing the set ofanticipation criteria (at block 852) and identifying the criterion (atblock 853) can include extracting (e.g., creating a derivation),examining (e.g., performing an inspection), evaluating (e.g., generatingan appraisal), dissecting (e.g., scrutinizing an attribute), resolving(e.g., ascertaining an observation/conclusion/answer), parsing (e.g.,deciphering a construct), querying (e.g., asking a question), searching(e.g., exploring for a reason), comparing (e.g., relating anassessment), classifying (e.g., assigning a designation), orcategorizing (e.g., organizing by a feature). Anticipation criteriaanalysis may include a process of inspecting, cleaning, transforming, ormodeling data to discover useful information, suggest conclusions, orsupport decisions. Anticipation criteria analysis can extractinformation/patterns from a data set and transform/translate it into anunderstandable structure (e.g., a data report which can be provided) forfurther use. The identified criterion can indicate to install thecandidate application bundle on the first compute node.

In embodiments, the set of anticipation criteria includes an expectedcandidacy criterion of the first compute node for processing the streamof tuples at block 861. The expected candidacy criterion of the firstcompute node for processing the stream of tuples can be related to alikelihood that the first compute node will be requested/utilized/neededto process the stream of tuples. For example, if the system is delugedwith a torrent of streams of tuple, the likelihood may increase. On theother hand, if an overall system load is decreasing the likelihood maydecrease. Thus, an indication that the likelihood will increase (e.g., anew job is submitted) can influence preloading. As another example, inresponse to the first compute node completing an earlier job,availability may indicate a proper scenario to preload because the firstcompute node can have more availability than other similarly-situatedcompute nodes.

In embodiments, the set of anticipation criteria includes an expectedresource burden criterion when processing the stream of tuples at block862. The expected resource burden criterion when processing the streamof tuples can be related to resource usage (e.g., processingspeed/power, memory levels) which is expected duringprocessing/computation. The expected resource burden criterion cancorrespond to a forecast/predicted value based on examples such ashistorical information, information with respect to the job/bundles, orother information. For instance, if the first compute node tends toburden resources less than other compute nodes when processing jobs thatuse a particular type of application bundle, the first compute node maybe identified/chosen for processing which utilizes the particular typeof application bundle. As such, preloading may be of benefit in a mannerwhich is consistent with a plurality of anticipation criteria (e.g.,expected candidacy criterion and expected resource burden criterion).

In embodiments, the set of anticipation criteria includes an expectedresource burden criterion when downloading the application bundle to thefirst compute node from the second compute node at block 863. Theexpected resource burden criterion when downloading the applicationbundle to the first compute node from the second compute node can berelated to resource usage (e.g., processing speed/power, memory levels)which is expected to be expended fordownloading/installation/deployment/allocation. The expected resourceburden criterion can correspond to a forecast/predicted value based onexamples such as historical information, information with respect to thejob/bundles, or other information. For instance, if the first computenode tends to burden resources less than other compute nodes whendownloading a particular type of application bundle, the first computenode may be identified/chosen for downloading which utilizes theparticular type of application bundle. As another example, bandwidthavailable (or historically conveyed) may indicate that a connectionbetween the first and second compute nodes would have less of a burdenon the overall system than another combination of compute nodes.

In embodiments, the set of anticipation criteria includes a physicalproximity criterion of the second compute node with respect to the firstcompute node at block 864. The physical proximity criterion can be basedon, for example, relative geographical nearness. Weighting of thephysical proximity criterion (e.g., weighting with respect to othercriteria) may be different for private cloud environments, public cloudenvironments, or hybrid cloud environments. For example, the physicalproximity criterion may be weighted with less importance in a privatecloud environment with storage all within one physicalstructure/building as opposed to greater weighting in a public cloudenvironment with storage across multiple servers deployed across acontinent. As such, in general, nearer compute nodes may be used;however, certain circumstances in a hybrid cloud environment, forexample, could be conducive to larger distances (e.g., for the purposesof keeping the application on either the public cloud or the privatecloud without using both).

In embodiments, the set of anticipation criteria includes a failoverfrequency criterion of the second compute node at block 865. Thefailover frequency criterion of the second compute node may indicate howoften the second compute nodes fails and would utilize a preloadedcompute node for processing the job in response to such failure. Thefailover frequency criterion may correlate to an expectation/need for afast processing element restart. The expectation/need can bescored/tiered with respect to an established/predetermined threshold. Inembodiments, communication may be opened with a job table to anticipatepotential job/processing element movement/shifting. A variety ofbenchmarks or thresholds may be used in combination with other criteriawhich, altogether, utilize the failover frequency criterion.

In embodiments, the set of anticipation criteria includes a temporalbenefit criterion for processing the stream of tuples within a thresholdtemporal period at block 866. The temporal benefit criterion forprocessing the stream of tuples within the threshold temporal period canindicate a proficiency of an efficiency for timeliness with respect toprocessing the stream of tuples. For example, if a job requests forprocessing of the stream of tuples to be completed within ten minutes(e.g., in order to move to the next phase of processing on-schedule),the temporal benefit criterion can indicate how often/frequent (e.g.,based on a historic expectation) processing will be completed in-time(e.g., so as to facilitate staying on-schedule). Preloading using thetemporal benefit criterion can assist in completing tasks on-schedule orahead-of-schedule which may provide a measurable benefit (e.g., in costsavings or marketable positive impacts). Altogether, the set ofanticipation criteria can be used interchangeably in combination with avariety of weighting methodologies being contemplated to provide one ormore permutations for preloading.

At block 870, the candidate application bundle is installed on the firstcompute node. In various embodiments, installation may include the firstcompute node “pulling” the application bundle from the second computenode. In certain embodiments, installation can include the secondcompute node “pushing” the application bundle to the first compute node.Installing the application bundle may include creating a localcopy/version of the application bundle on the first compute node.Installing the application bundle can generate a like version of theapplication bundle by a piecemeal methodology (e.g., fit togethervarious components). Installation can have a verification phase whichverifies operability or completeness. Installation may includeunwrapping a wrapped data packet (e.g., which has encryption technologyfor security/transmission purposes).

In embodiments, the stream of tuples to be processed by a plurality ofprocessing elements operating on a set of compute nodes can be receivedat block 880. The stream of tuples may be received consistent with thedescription herein including FIGS. 1-6. Current/future processing by theplurality of processing elements may be performed consistent with thedescription herein including FIGS. 1-6. The set of compute nodes mayinclude a shared pool of configurable computing resources. For example,the set of compute nodes can be a public cloud environment, a privatecloud environment, or a hybrid cloud environment. In certainembodiments, each of the set of compute nodes are physically separatefrom one another.

In embodiments, using the candidate application bundle on the firstcompute node, the stream of tuples may be processed at block 890. Thestream of tuples may be processed subsequent to/in response toinstalling the candidate application bundle on the first compute node(see block 870). As such, the candidate application bundle can bepreloaded with respect to processing the stream of tuples (e.g.,temporally distinct by a threshold temporal period). The applicationbundle, having been preloaded onto the first compute node, can processthe stream of tuples without a temporal lag (e.g., due to waiting forthe application bundle to be downloaded). The stream of tuples may beprocessed consistent with the description herein including FIGS. 1-6.Processing, using local management on the first compute node, of thestream of tuples may provide various flexibilities for the set ofcompute nodes.

Method 800 concludes at block 899. Aspects of method 800 may provideperformance or efficiency benefits for managing a set of applicationbundles for processing a stream of tuples. For example, aspects ofmethod 800 may include positive impacts with respect to speed orresponsiveness by preloading a particular application bundle in a filesystem. Altogether, performance or efficiency benefits when managing aset of application bundles may occur (e.g., speed, flexibility,responsiveness, resource usage).

FIG. 9 is a flowchart illustrating a method 900 for managing anapplication bundle for processing a stream of tuples according toembodiments. Aspects can manage application bundle(s) (in a file system)by a local host which is distributed/separate from a central controlhost or a remote host that execution is intended for with respect to theapplication bundle(s). Staleness latency of application bundle(s) mayoccur using a methodology which leaves/keeps application bundle(s) on acompute node longer than the application bundle is (initially) needed.Aspects may allow for quick response (e.g., when requested forutilization) due to a relative lack of lag time to provision theapplication bundle(s). Method 900 may begin at block 901.

At block 910, a stream of tuples is received. The stream of tuples is tobe processed by a plurality of processing elements operating on a set ofcompute nodes. The stream of tuples may be received consistent with thedescription herein including FIGS. 1-6. Current/future processing by theplurality of processing elements may be performed consistent with thedescription herein including FIGS. 1-6. The set of compute nodes mayinclude a shared pool of configurable computing resources. For example,the set of compute nodes can be a public cloud environment, a privatecloud environment, or a hybrid cloud environment. In certainembodiments, each of the set of compute nodes are physically separatefrom one another.

At block 920, the stream of tuples is processed. The stream of tuples isprocessed on a first compute node using the application bundle. Thestream of tuples may be processed consistent with the description hereinincluding FIGS. 1-6. The application bundle can process the stream oftuples without involvement from another compute node. Processing, usinglocal management on the first compute node, of the stream of tuples mayprovide various flexibilities for the set of compute nodes.

At block 930, a staleness factor with respect to the application bundleis detected. The staleness factor is detected by the first compute node.Staleness can refer to a lack of freshness or relevancy. Detecting astaleness factor can include sensing a triggering event. For example,the staleness factor may include a temporal period which the applicationbundle is not in an execution state. The triggering event can include abenchmark temporal period which can be sensed/triggered when itmatches/meets/reaches/exceeds the temporal period. In embodiments, thetriggering event can be related aspects which are at least partiallynon-temporal.

In embodiments, the staleness factor with respect to the applicationbundle interrelates with the plurality of processing elements at block933. For instance, the application bundle may be on the first computenode when the plurality of processing elements areinitiated/started/commenced. The application bundle may be left/kept onthe first compute node while processing by the plurality of processingelements occurs. The staleness factor may be based on a frame ofreference from when processing begins, reaches a benchmark such as aquartile proportion, or finishes. For instance, in certain embodimentsthe application bundle may be removed once zero processing elements arerunning on that host (e.g., high file space usage and low expectationfor processing element restart). In various embodiments, the applicationbundle may be kept for a temporal keeping period (e.g., waiting for atleast three hours) until removing/deleting the application bundle due tonon-use by a running processing element. The application bundle may bekept around to support the possibility of a new computer processingelement being rescheduled onto the host (e.g., a computer processingelement from an existing job, or a computer processing element from anewly submitted job).

In embodiments, detecting the staleness factor with respect to theapplication bundle includes a comparison at block 937 and adetermination at block 938. An activity value of the application bundlecan be compared with a threshold value at block 937. The activity valuemay be determined to be below the threshold value at block 938. Forexample, if the activity value is very low (e.g., 3 on a scale of 0 to100) and the threshold value is greater (e.g., 10 on the scale of 0 to100), the comparison may lead to a determination such as nearlycompletely stale. An algorithm which may be non-linear can be used suchas analyzing using a normal distribution or a parabolic expression. Anearly completely stale application bundle may be kept for a shorteramount time than a somewhat stale application bundle. When resources arenot limited, the application bundle may be kept longer. When resourcesare limited, a nearly completely stale application bundle can be removedin response to the determination it is nearly completely stale. Theactivity value may represent how inactive the application bundle hasbeen in order to assist in determining how long to keep it on its hostcompute node.

At block 950, the application bundle on the first compute node ismaintained. Maintain can include preserve, protect, sustain, uphold,renew, save, prolong, carry-on, or support. For example, the applicationbundle may be continued to be supported on the first compute node so asto sustain a prolonged ability to carry-on new operations. Maintainingthe application bundle on the first compute node is based on amaintenance criterion related to both the application bundle and theplurality of processing elements. In embodiments, maintaining theapplication bundle includes a preservation (e.g., sustaining) at block952 and a prevention (e.g., blocking, suppressing, precluding, stopping,prohibiting, disregarding, at least deterring) at block 953. Theapplication bundle may be preserved/kept on the first compute node atblock 952. Removal/deletion of the application bundle from the firstcompute node can be prevented at block 953.

In embodiments, the maintenance criterion includes a threshold temporalvalue at block 956. The threshold temporal value may include a policychoice to keep an application bundle for (at least) a specified amountof time (e.g., three hours). As such, the removal of the applicationbundle may occur only after the specified amount of time of an event(e.g., non-use) by a running processing element. The threshold temporalvalue may include a policy choice to keep an application bundle until aspecified time (e.g., 08:00:00 AM). As such, the removal of theapplication bundle may occur only after the clock reaches the specifiedtime. For example, the application bundle could be kept for overnightprocessing that may commence but removed at the beginning of a workdaywhen resource demands may change. In embodiments, the threshold temporalvalue may be based on profile information for aspects of the system(e.g., historical information, predictive information, compute nodehistory, job processing history, processing element usage).

In embodiments, the maintenance criterion includes a threshold computingresources value at block 957. The threshold computing resources valuemay correlate with a police choice to utilize system resource factors toat least assist in determining staleness latency policy. For example, anapplication bundle may be maintained on a local compute node whencomputing resources are being used heavily (e.g., so as to not useresources removing/reinstalling the application bundle). In certainsituations, however, a cost-benefit analysis may determine that it wouldbe more efficient or lead to higher performance to expend the computingresources at a given marker in order to free up resources for a futuremarker. Another example may include not removing the application bundleuntil file space usage is greater than a threshold (e.g., keeping theapplication bundle until file space usage reaches 75% thereby having adesire for more space at that point). A processing factor or a memoryfactor for the local compute node are considered, as are a variety offactors related to bandwidth, flexibility, etc.

In embodiments, the maintenance criterion includes a thresholdprocessing element accessibility value at block 958. The thresholdprocessing element accessibility value may correlate with a policychoice having a desire/need for an efficient/fast processing elementactivation/restart. For example, a high-priority set of processingelements may have an accessibility policy which indicates that keepingstale application bundle(s) could prove useful (e.g., for a failover,for altering processing). To illustrate, using a scale of 0 to 100, athreshold processing element accessibility value may be set by a user to80. When a high-priority set of processing elements is running onanother compute node, a score of 90 may indicate to keep a stale versionof the application bundle on the first compute node; however, alow-priority set of processing elements may have a score of 20 which canindicate not to keep stale application bundles. As such, the thresholdprocessing element accessibility value can be indicative of a potentialweightings with respect to security, performance, efficiency, etc.

In embodiments, the plurality of processing elements can be activated atblock 971. Activating can include starting/restarting, stimulating, orprompting. When activating the plurality of processing elements, theapplication bundle may be used at block 972. The application bundlebeing used can be one or more which were considered stale but notdeleted/removed. As such, staleness latency can be illustrated withrespect to the application bundle by a sleep/hibernation phase followedby a resume/commencement phase.

Method 900 concludes at block 999. Aspects of method 900 may provideperformance or efficiency benefits for managing an application bundlefor processing a stream of tuples. For example, aspects of method 900may include positive impacts with respect to responsiveness bymaintaining a stale application bundle in a file system (e.g., stalenesslatency). Altogether, performance or efficiency benefits when managingan application bundle may occur (e.g., speed, flexibility,responsiveness, resource usage).

FIG. 10 is a flowchart illustrating a method 1000 for managing anapplication bundle for processing a stream of tuples according toembodiments. Aspects can manage application bundle(s) (in a file system)by a local host which is distributed/separate from a central controlhost or a remote host that execution is intended for with respect to theapplication bundle(s). Backup of application bundle(s) (e.g.,application bundle redundancy) may occur using a “pull” methodology.Backup can occur using a set of application bundle data. Aspects maybackup an application bundle on a compute node based onsusceptibility/vulnerability of the application bundle and provide anavailability/reliability benefit such as providing another candidatesource compute node for provisioning of the application bundle. Method1000 may begin at block 1001.

At block 1010, a first compute node monitors the set of applicationbundle data related to both a set of compute nodes and the applicationbundle. For example, the first compute node may communicate with anapplication bundle directory (e.g., having the set of application bundledata). The application bundle directory can be stored separate from theset of compute nodes (e.g., the first and second compute nodes). Inembodiments, monitoring the set of application bundles may includeobserving the set of application bundle data at block 1012. Also,monitoring the set of application bundles can include analyzing the setof application bundle data at block 1014. In certain embodiments,observing and analyzing can include aspects which receive an element andprocess the element that was received.

For instance, observing (at block 1012) can include querying (e.g.,asking a question), searching (e.g., exploring for a reason), obtaining(e.g., recording a collection), probing (e.g., checking a property),scanning (e.g., reviewing a sample), or tracking (e.g., following acharacteristic). To illustrate, observing may include locating, in theset of application bundle data, a group of specialized applicationbundles which can interpret the particularized subset of information tobe found in tuples expected to be received for processing within aquickly-approaching temporal period.

For instance, analyzing (at block 1014) can include extracting (e.g.,creating a derivation), examining (e.g., performing an inspection),dissecting (e.g., scrutinizing an attribute), parsing (e.g., decipheringa construct), comparing (e.g., relating an assessment), or classifying(e.g., assigning a designation). Data analysis may include a process ofinspecting, cleaning, transforming, or modeling data to discover usefulinformation, suggest conclusions, or support decisions. Data analysiscan extract information/patterns from a data set and transform/translateit into an understandable structure (e.g., a data report which can beprovided) for further use.

At block 1030, the first compute node determines that the applicationbundle is installed on fewer than a threshold number of compute nodes.The determination is made based on the set of application bundle data.The determination may be made by performing a comparison with respect tothe threshold number of compute nodes and the set of application bundledata. For example, a count (of compute nodes having the applicationbundle installed) from the set of application bundle data may becalculated and subsequently compared with the threshold number ofcompute nodes. The threshold number of compute nodes can represent anumber of candidate sources for the application bundle. For example, apolicy decision can be made for how many candidate sources should beavailable for application bundle provisioning. The policy decision maybe based on a nature to which a job would be affected if a host fails(e.g., recovery implications). In embodiments, determining theapplication bundle is installed on fewer than the threshold number ofcompute nodes includes an analysis at block 1032 and an identificationat block 1033.

The set of application bundle data may be analyzed at block 1032. Forinstance, analyzing can include examining (e.g., performing aninspection of host/bundle-data), evaluating (e.g., generating anappraisal of host/bundle-data), resolving (e.g., ascertaining anobservation/conclusion/answer with respect to host/bundle-data), parsing(e.g., deciphering a construct for the host/bundle-data), querying(e.g., asking a question regarding host/bundle-data), or categorizing(e.g., organizing by a feature of the host/bundle-data). A criterion canbe identified at block 1033. The criterion may be identified in responseto and based on the analysis at block 1032. The criterion can indicateto retrieve the application bundle from the second compute node. Thecriterion may be related to a variety of factors discussed herein. Forinstance, the criterion may correlate with an advantageous/favorablesource (e.g., provides overall performance or efficiency benefits forthe system by sourcing from a more favorable source rather than a lessfavorable source).

In embodiments, the threshold number of compute nodes is based on anunavailability burden criterion at block 1036. The unavailability burdencriterion can indicate a challenge to the system if the applicationbundle became unavailable (e.g., by failure of all of the compute nodeswhich have the application bundle). Unavailability may indicate a totallack of availability (e.g., complete shutdown) or an effective lack ofavailability (e.g., challenged to process a threshold quantity of data).For example, if high availability is very important, three sources maybe the threshold number of compute nodes in order to be able to recoverfrom two hosts failing. As such, a bundle redundancy operation (e.g.,backup) would be appropriate if fewer than three sources exist.Accordingly, if high availability is moderately important, two sourcesmay be the threshold number of compute nodes in order to be able torecover from one host failing. As such, a bundle redundancy operation(e.g., backup) would be appropriate if fewer than two sources exist.

In embodiments, the threshold number of compute nodes is based on anexpected resource burden criterion at block 1037. The expected resourceburden criterion can indicate a challenge to system resources withrespect to future processing. A resource burden may include bandwidth,processing speed/power, memory factors, etc. An expected resource burdenmay have a predicted/forecast value for the resource burden. Theexpected resource burden may be based on profile information (e.g.,historical, random, predetermined). For example, the expected resourceburden criterion can utilize a comparison of a component of the expectedresource burden with a threshold value (e.g., which may be related tothe profile information). As such, if processing power is expected to belimited due to high usage, an additional host having the applicationbundle may be of benefit so long as it can be downloaded/installed priorto the period of high usage. Accordingly, backing-up the applicationbundle based on the expected resource burden criterion indicating threesources may be useful tomorrow could initiate an action to add aredundant application bundle when only two hosts currently have theapplication bundle. In certain embodiments, it may be desirable toimmediately back-up the application bundle upon start-up so that errorevent(s) that occur relatively early in a processing process do notcause substantial delay(s) due to a lack of resources.

In embodiments, the threshold number of compute nodes is based on afailover frequency criterion at block 1038. The failover frequencycriterion can indicate a failure rate. The failure rate may be for theset of compute nodes, for a particular compute node such as the secondcompute node, or the application bundle. In certain embodiments, thefailure rate may include a typology for such aspects (e.g., a type ofapplication bundle, a type of compute node). For example, a largerthreshold number of compute nodes may be determined for compute nodesthat are challenged more frequently (e.g., higher failure rate). Assuch, more redundant application bundles on more compute nodes may begenerated (e.g., by pulling the application bundle from a remote host toa local host). For stable application bundles and compute nodes, thethreshold number of compute nodes may be lower (e.g., lower failoverfrequency). In this way, performance or efficiency benefits may result.

In embodiments, the threshold number of compute nodes is based on atemporal benefit criterion for processing the stream of tuples within athreshold temporal period at block 1039. The temporal benefit criterioncan indicate a benefit/value (e.g., cost savings, energy savings,contractual incentives) arising out of timely performance for processingthe stream of tuples within a threshold temporal period (e.g., withinone hour, by midnight, before the end of the third quarter of the fiscalyear). The benefit/value may have one or more tiers/levels which canrelate to a desire for bundle redundancy. For example, a significantbenefit/value can correlate to a higher desire for backups than anegative or no benefit/value for performing a task in a timely manner.As such, if an agreement provides a 150% incentive for early performanceof a task by one day, the temporal benefit criterion may be determinedto increase the threshold number of compute nodes so that the earlierperformance is more likely to be met (e.g., as opposed to a task thatpresents no benefit for early performance where bundle redundancy may beless likely to benefit the system). A variety ofcombinations/permutations of embodiments incorporating aspects of thevarious criteria are contemplated (e.g., unavailability burden criterionplus failover frequency criterion).

At block 1050, the application bundle is retrieved. The applicationbundle may be retrieved by the first compute node (e.g., pulled by thefirst compute node from the second compute node). The application bundlemay be retrieved in response to determining the application bundle isinstalled on fewer than a threshold number of compute nodes. Retrievingthe application bundle may include copying or moving the applicationbundle. Retrieving the application bundle can include at least one oftransmitting a request for the application bundle, obtaining theapplication bundle, or receiving the application bundle. In certainembodiments, retrieving can include selectively retrieving chosenelements of the application bundle to subsequently generate a likeversion of the application bundle by a piecemeal methodology.

At block 1070, the application bundle is installed on the first computenode (in response to retrieving the application bundle). The firstcompute node manages the installation. Using the first compute node tomanage the installation can facilitate the pull methodology. Inembodiments, managing the installation can include, for example,retrieval aspects (see block 735 of FIG. 7). Installing may includeinitiation/commencement of adeployment/placement/installation/allocation. Installing the applicationbundle may include creating a local copy/version of the applicationbundle on the first compute node. Installing the application bundle cangenerate a like version of the application bundle by a piecemealmethodology (e.g., fit together various components). Installation canhave a verification phase which verifies operability or completeness.Installation may include unwrapping a wrapped data packet (e.g., whichhas encryption technology for security/transmission purposes).

In embodiments, the stream of tuples to be processed by a plurality ofprocessing elements operating on a set of compute nodes can be receivedat block 1080. The stream of tuples may be received consistent with thedescription herein including FIGS. 1-6. Current/future processing by theplurality of processing elements may be performed consistent with thedescription herein including FIGS. 1-6. The set of compute nodes mayinclude a shared pool of configurable computing resources. For example,the set of compute nodes can be a public cloud environment, a privatecloud environment, or a hybrid cloud environment. In certainembodiments, each of the set of compute nodes are physically separatefrom one another.

In embodiments, using the application bundle on the first compute node,the stream of tuples may be processed at block 1090. The stream oftuples may be processed subsequent to/in response to installing thecandidate application bundle on the first compute node (see block 1070).As such, the candidate application bundle can be backed-up so as to beready for use when called-upon. The stream of tuples may be processedconsistent with the description herein including FIGS. 1-6. Processing,using local management on the first compute node, of the stream oftuples may provide various flexibilities for the set of compute nodes.

Method 1000 concludes at block 1099. Aspects of method 1000 may provideperformance or efficiency benefits for managing an application bundlefor processing a stream of tuples. For example, aspects of method 1000may include positive impacts with respect to reliability (e.g., toprocess a higher ratio of tuples without overlooking a threshold numberof tuples) by backing-up a particular application bundle in a filesystem. Altogether, performance or efficiency benefits when managing anapplication bundle may occur (e.g., speed, flexibility, responsiveness,resource usage).

In addition to embodiments described above, other embodiments havingfewer operational steps, more operational steps, or differentoperational steps are contemplated. Also, some embodiments may performsome or all of the above operational steps in a different order. Themodules are listed and described illustratively according to anembodiment and are not meant to indicate necessity of a particularmodule or exclusivity of other potential modules (or functions/purposesas applied to a specific module).

In the foregoing, reference is made to various embodiments. It should beunderstood, however, that this disclosure is not limited to thespecifically described embodiments. Instead, any combination of thedescribed features and elements, whether related to differentembodiments or not, is contemplated to implement and practice thisdisclosure. Many modifications and variations may be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiments. Furthermore, although embodiments of thisdisclosure may achieve advantages over other possible solutions or overthe prior art, whether or not a particular advantage is achieved by agiven embodiment is not limiting of this disclosure. Thus, the describedaspects, features, embodiments, and advantages are merely illustrativeand are not considered elements or limitations of the appended claimsexcept where explicitly recited in a claim(s).

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

Embodiments according to this disclosure may be provided to end-usersthrough a cloud-computing infrastructure. Cloud computing generallyrefers to the provision of scalable computing resources as a serviceover a network. More formally, cloud computing may be defined as acomputing capability that provides an abstraction between the computingresource and its underlying technical architecture (e.g., servers,storage, networks), enabling convenient, on-demand network access to ashared pool of configurable computing resources that can be rapidlyprovisioned and released with minimal management effort or serviceprovider interaction. Thus, cloud computing allows a user to accessvirtual computing resources (e.g., storage, data, applications, and evencomplete virtualized computing systems) in “the cloud,” without regardfor the underlying physical systems (or locations of those systems) usedto provide the computing resources.

Typically, cloud-computing resources are provided to a user on apay-per-use basis, where users are charged only for the computingresources actually used (e.g., an amount of storage space used by a useror a number of virtualized systems instantiated by the user). A user canaccess any of the resources that reside in the cloud at any time, andfrom anywhere across the Internet. In context of the present disclosure,a user may access applications or related data available in the cloud.For example, the nodes used to create a stream computing application maybe virtual machines hosted by a cloud service provider. Doing so allowsa user to access this information from any computing system attached toa network connected to the cloud (e.g., the Internet).

Embodiments of the present disclosure may also be delivered as part of aservice engagement with a client corporation, nonprofit organization,government entity, internal organizational structure, or the like. Theseembodiments may include configuring a computer system to perform, anddeploying software, hardware, and web services that implement, some orall of the methods described herein. These embodiments may also includeanalyzing the client's operations, creating recommendations responsiveto the analysis, building systems that implement portions of therecommendations, integrating the systems into existing processes andinfrastructure, metering use of the systems, allocating expenses tousers of the systems, and billing for use of the systems.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the foregoing is directed to exemplary embodiments, other andfurther embodiments of the invention may be devised without departingfrom the basic scope thereof, and the scope thereof is determined by theclaims that follow. The descriptions of the various embodiments of thepresent disclosure have been presented for purposes of illustration, butare not intended to be exhaustive or limited to the embodimentsdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiments. The terminology used herein was chosen toexplain the principles of the embodiments, the practical application ortechnical improvement over technologies found in the marketplace, or toenable others of ordinary skill in the art to understand the embodimentsdisclosed herein.

1. A computer-implemented method for managing an application bundle forprocessing a stream of tuples, the method comprising: monitoring, by afirst compute node, a set of application bundle data related to both aset of compute nodes and the application bundle; determining, by thefirst compute node based on the set of application bundle data, theapplication bundle is installed on fewer than a threshold number ofcompute nodes; retrieving, by the first compute node in response todetermining the application bundle is installed on fewer than athreshold number of compute nodes, the application bundle; andinstalling, by the first compute node in response to retrieving theapplication bundle, the application bundle on the first compute node. 2.The method of claim 1, further comprising: receiving the stream oftuples to be processed by a plurality of processing elements operatingon the set of compute nodes; and processing, using the applicationbundle on the first compute node, the stream of tuples in response toinstalling the application bundle on the first compute node.
 3. Themethod of claim 1, wherein determining, by the first compute node basedon the set of application bundle data, the application bundle isinstalled on fewer than the threshold number of compute nodes includes:analyzing the set of application bundle data; and identifying acriterion which indicates to retrieve the application bundle.
 4. Themethod of claim 1, wherein the threshold number of compute nodes isbased on an unavailability burden criterion.
 5. The method of claim 1,wherein the threshold number of compute nodes is based on an expectedresource burden criterion.
 6. The method of claim 1, wherein thethreshold number of compute nodes is based on a failover frequencycriterion.
 7. The method of claim 1, wherein the threshold number ofcompute nodes is based on a temporal benefit criterion for processingthe stream of tuples within a threshold temporal period.
 8. The methodof claim 1, wherein monitoring, by the first compute node, the set ofapplication bundle data related to both the set of compute nodes and theapplication bundle includes: observing the set of application bundledata; and analyzing the set of application bundle data. 9-20. (canceled)